title: Network meta-analysis of randomized clinical trials: reporting the proper summaries.
abstract: BACKGROUND
  In the absence of sufficient data directly comparing multiple treatments, indirect comparisons using network meta-analyses (NMAs) can provide useful information. Under current contrast-based (CB) methods for binary outcomes, the patient-centered measures including the treatment-specific event rates and risk differences (RDs) are not provided, which may create some unnecessary obstacles for patients to comprehensively trade-off efficacy and safety measures.
  
  
  OBJECTIVE
  We aim to develop NMA to accurately estimate the treatment-specific event rates.
  
  
  METHODS
  A Bayesian hierarchical model is developed to illustrate how treatment-specific event rates, RDs, and risk ratios (RRs) can be estimated. We first compare our approach to alternative methods using two hypothetical NMAs assuming a fixed RR or RD, and then use two published NMAs to illustrate the improved reporting.
  
  
  RESULTS
  In the hypothetical NMAs, our approach outperforms current CB NMA methods in terms of bias. In the two published NMAs, noticeable differences are observed in the magnitude of relative treatment effects and several pairwise statistical significance tests from previous report.
  
  
  CONCLUSIONS
  First, to facilitate the estimation, each study is assumed to hypothetically compare all treatments, with unstudied arms being missing at random. It is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to 'nonignorable missingness' and potentially bias our estimates. Second, we have not considered methods to identify and account for potential inconsistency between direct and indirect comparisons.
  
  
  CONCLUSIONS
  The proposed NMA method can accurately estimate treatment-specific event rates, RDs, and RRs and is recommended.
authors.affiliation: aDivision of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Bradley P
authors.initials: BP
authors.lastname: Carlin
authors.forename: James D
authors.initials: JD
authors.lastname: Neaton
authors.forename: Guoxing G
authors.initials: GG
authors.lastname: Soon
authors.forename: Lei
authors.initials: L
authors.lastname: Nie
authors.forename: Robert
authors.initials: R
authors.lastname: Kane
authors.forename: Beth A
authors.initials: BA
authors.lastname: Virnig
authors.forename: Haitao
authors.initials: H
authors.lastname: Chu
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D016032:Randomized Controlled Trials as Topic